Overall Statistics |
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -0.469 Tracking Error 0.346 Treynor Ratio 0 Total Fees $0.00 |
from datetime import datetime,timedelta import numpy as np class ReversalAlpha(QCAlgorithm): def Initialize(self): self.SetStartDate(2020, 1, 30) # Set Start Date self.SetCash(100000) # Set Strategy Cash tickers = ["EURUSD","USDCAD"] # Rolling Windows to hold bar close data keyed by symbol self.closingData = {} for ticker in tickers: symbol = self.AddForex(ticker, Resolution.Hour, Market.Oanda).Symbol self.closingData[symbol] = RollingWindow[float](50) # Warm up our rolling windows self.SMA45 = SimpleMovingAverage(symbol, 45) self.SetWarmUp(50) def OnData(self, data): for symbol, window in self.closingData.items(): if data.ContainsKey(symbol) and data[symbol] is not None: window.Add(data[symbol].Close) if self.IsWarmingUp or not all([window.IsReady for window in self.closingData.values()]): return for symbol, window in self.closingData.items(): supports, resistances = self.GetPriceLevels(window) #self.Log(f"Symbol: {symbol.Value} , Supports: {supports} , Resistances: {resistances}") self.Debug(self.SMA45.Current.Value) if self.SMA45.Current.Value<0: self.marketTicket=self.MarketOrder(symbol, -100000) def GetPriceLevels(self, series, variation = 0.005, h = 3): supports = [] resistances = [] maxima = [] minima = [] # finding maxima and minima by looking for hills/troughs locally for i in range(h, series.Size-h): if series[i] > series[i-h] and series[i] > series[i+h]: maxima.append(series[i]) elif series[i] < series[i-h] and series[i] < series[i+h]: minima.append(series[i]) # identifying maximas which are resistances for m in maxima: r = m * variation # maxima which are near each other commonLevel = [x for x in maxima if x > m - r and x < m + r] # if 2 or more maxima are clustered near an area, it is a resistance if len(commonLevel) > 1: # we pick the highest maxima if the cluster as our resistance level = max(commonLevel) if level not in resistances: resistances.append(level) # identify minima which are supports for l in minima: r = l * variation # minima which are near each other commonLevel = [x for x in minima if x > l - r and x < l + r] # if 2 or more minima are clustered near an area, it is a support if len(commonLevel) > 1: # We pick the lowest minima of the cluster as our support level = min(commonLevel) if level not in supports: supports.append(level) return supports, resistances